Image Compression and Transmission System Based on Narrowband Satellite Internet of Things

The Satellite Internet of Things (SatIoT) enables integrated space–air–ground connectivity and information exchange through satellite communication networks and distributed sensors. Its primary advantage lies in achieving global coverage, effectively addressing communication challenges in signal-deprived regions. However, image transmission in SatIoT systems is severely constrained by limited satellite bandwidth, placing high demands on the efficiency and adaptability of compression algorithms. To address these challenges, this work adopts a co-design approach at both the algorithmic and system levels. At the algorithmic level, we implement a deep learning-based image compression framework. To improve the Rate-Distortion performance of the algorithm, 1) we propose a plug-and-play feature extraction module that integrates the strengths of Transformer and CNN to capture both global and local features, preserving critical image details while eliminating redundancy. 2) A Multi Channel Scaling Enhancement module is proposed, which can efficiently fuse features without performance loss and reduce the computational overhead through channel scaling. The experimental results show that they significantly improve the Rate-Distortion performance of mainstream compression algorithms with relatively low computational consumption. At the system level, we design a complete SatIoT communication chain comprising an edge terminal, the Tiantong satellite, and a ground receiving terminal. The optimized compression algorithm is deployed on the edge terminal to enable edge computing, supporting image acquisition, compression, and satellite-based transmission. The system achieves reliable, end-to-end image transmission from signal-deprived regions to ground networks, greatly improving the efficiency and robustness of satellite-based image delivery.